Search (8 results, page 1 of 1)

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Abstract

Das Relevanz-Ranking im Katalog der Universitätsbibliothek Heidelberg HEIDI, bereits 2009 in einem Beitrag in dieser Zeitschrift beschrieben, wurde in den letzten Jahren durch neue Entwicklungen und Methoden stark verbessert. Der Aufsatz beschreibt die Realisierung der bisherigen Rankingmaßnahmen unter der neu eingesetzten Suchmaschinenplattform SOLR. Weiter werden verschiedene neue Möglichkeiten für Rankinganpassungen unter SOLR sowie deren Einsatz im HEIDI-Katalog dargestellt.

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Content

Vgl.: doi: 10.1016/j.ipm.2013.08.007. A short preliminary version of this paper was published in the proceeding of WWW 2009 as a two page poster paper.

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Date

22. 8.2014 17:05:18

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Date

13. 9.2014 14:45:22

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Date

20. 1.2015 18:30:22
18. 9.2018 18:22:56

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Abstract

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text-based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag-of-words approaches, in which the image retrieval problem turns into one of standard text-based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical-image-dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image-relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image-relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state-of-the-art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

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Date

11. 6.2012 14:22:34

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Date

22. 3.2013 19:34:49